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Emergency vehicle (EMV) service is a key function of cities and isexceedingly challenging due to urban traffic congestion. The main reason behindEMV service delay is the lack of communication and cooperation between vehiclesblocking EMVs. In this paper, we study the improvement of EMV service under V2Xconnectivity. We consider the establishment of dynamic queue jump lanes (DQJLs)based on real-time coordination of connected vehicles in the presence ofnon-connected human-driven vehicles. We develop a novel Markov decision processformulation for the DQJL coordination strategies, which explicitly accounts forthe uncertainty of drivers' yielding pattern to approaching EMVs. Based onpairs of neural networks representing actors and critics for agent vehicles, wedevelop a multi-agent actor-critic deep reinforcement learning algorithm thathandles a varying number of vehicles and a random proportion of connectedvehicles in the traffic. Approaching the optimal coordination strategies viaindirect and direct reinforcement learning, we present two schemata to addressmulti-agent reinforcement learning on this connected vehicle application. Bothapproaches are validated, on a micro-simulation testbed SUMO, to establish aDQJL fast and safely. Validation results reveal that, with DQJL coordinationstrategies, it saves up to 30% time for EMVs to pass a link-level intelligenturban roadway than the baseline scenario.

Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach